Development of an Industry 4.0-Based Analytical Method for the Value Stream Centered Optimization of Demand-Driven Warehousing Systems

Abstract

In real life situations, the material handling strategy of on-site storage systems is usually determined during the design of the storage system, which is no longer reviewed later. The strategy is typically determined by the person(s) designing the storage system, without the use of scientific methods, based on previous experience. Without a thorough periodic review of operational strategy, most companies’ warehousing systems have significant logistical losses (e.g., unnecessary material handling, waiting, operations), which also negatively affects the sustainability of the logistics operations. Therefore, eliminating these losses can increase both the competitiveness and the sustainability of companies. For this reason, the aim of this publication is to introduce a gap-filling test method that allows the selection of an optimal material handling strategy covering the total value stream in a demand-driven storage environment, using the opportunities offered by the Industry 4.0 concept, in particular in the field of big data analysis. This integrated approach has so far not emerged in the study of warehouse material handling strategies. Beyond the obvious economic benefits, the application of this method can clearly help companies to achieve a higher level of sustainability in their logistics operations, as it allows storage systems to operate more efficiently while minimizing material handling losses, ultimately resulting in a lesser demand for energy and raw materials. Moreover, this can also result in a reduction in the human and machine resources required to perform the tasks.

Industrial Internet of Things (IIoT) and Other Industry 4.0 Technologies in Spare Parts Warehousing in the Oil and Gas Industry: A Systematic Literature Review

“…Improper use of sensors in IoT/IIoT Lo et al [82] Collision of RFID tags for IoT Zhong [83] Lack of programmability; lack of software definition; lack of scalability Zhang et al [84] Security issues; integration of new technology with existing ones; return of investment on new technology Hamdy et al [85] Integration; successful transition from manual to digital; managing suppliers and distributors in the new digital system; reducing overall process time Tahir et al [86] Laws, regulations, and policies regarding information sharing that can negatively affect IoT usage Geng et al [87] Limited profitability in using Industry 4.0 technology in manufacturing when producing few items Terelak et al [88] Privacy concerns over digital access; delays in work during downtime; network bandwidth; high energy consumption; interrupted service; resource constraints Alwakeel [89] Lack of purpose and desired business outcome in a company regarding IIoT usage Liu et al [90] Increased electricity usage; increased maintenance costs; job losses; large initial investment; cyber security concerns with shared data Nantee et al [91] Communication delays; sensor interference; hardware faults, especially when relating to automated guided vehicles (AGVs) Chi et al [92] Increased workload when there are few items to work with Dobos et al [93] Outdated supply chain strategies not suitable for new business environments with IoT Chen et al [94] Granting access to appropriate users without compromising information and cyber security Ho et al [52] Various technical, organizational, and ergonomic challenges (especially in relation to augmented reality (AR)) Rejeb et al [95] Complexity of IoT can result in improper usage and technical difficulties Vukicevic et al [96] Lack of readiness for automation and digitization Zoubek et al [97] Lack of professionals with thorough competence in information technology-enabled logistics Wang [98] Lack of an appropriate analytical framework upon which to base IoT usage Tannady et al [32] Table 4 highlights the fear of industrial managers: the potential benefits of technology implementation also constitute the challenges. In Table 2, reduced costs were cited as a benefit.…”

Research Problems of Modern Warehouse Management Systems

“…synthesis of cyber-physical production systems (CPPS), Industrial Internet of Things (IIoT), robotics, artificial intelligence (AI), Cloud Computing (CC) and modern technologies that provide economic efficiency of production [1]. One of the important component of the organization of any production process is control (Supply Chain Management (SCM)), where Warehousing plays the main role [2]. Warehousing 4.0 -the concept of an automated intelligent warehouse solution for tasks such as automatic replenishment of stocks, autonomous identification of stocks, order picking and routing of delivery systems for loading and unloading using group control of robotic autonomous vehicles.…”